计算机工程2024，Vol.50Issue(1)：348-356,9.DOI:10.19678/j.issn.1000-3428.0067078
基于各向异性注意力的双分支血管分割模型
Dual-Branch Vascular Segmentation Model Based on Anisotropic Attention
摘要
Abstract
Vascular segmentation is significant for diagnosing and treating vascular diseases.However,because of the fuzzy boundary of vessels,the variable shape of diseased vessels,and the significant differences between different samples,the segmentation model should accurately determine the differences between vessels and background classes and analyze the connectivity within vessels.This study proposes a novel three-dimensional vascular segmentation network,CAU-Net,based on centerline constraints and anisotropic attention.In response to the difficulties in vascular segmentation,the basic network structure,ResU-Net,is improved to construct an anisotropic attention module.This module extracts vascular spatial anisotropic features from three directions based on the unique spatial anisotropy of the vascular structure and models the correlation between feature channels to learn the three-dimensional spatial information of the vessels.By using the main auxiliary dual-branch model,b-Net performs semantic segmentation on vessels,whereas a-Net learns the continuity features of vessel centerlines,constrains the vascular segmentation results of b-Net,and ensures the integrity of the vascular segmentation results.The experimental results on the publicly available dataset 3D-IRCADb-01 shows that for the segmentation of portal and hepatic veins,CAU-Net achieves Dice coefficients of(74.80±8.05)%and(76.14±6.89)%,NSD coefficients of(54.80±8.09)%and(50.40±5.22)%,clDice coefficients of(72.43±8.26)%and(70.84±6.05)%,Branch Detection(BD)rates of(46.47±12.89)%and(39.19±7.97)%,and Tree length Detection(TD)rates of(67.08±15.59)%and(61.47±9.32)%,respectively.Component ablation experiments are conducted on the publicly available cerebrovascular dataset IXI,and the average Dice,NSD,clDice,BD,and TD values of the model on the validation set are(94.11±0.39)%,(96.53±0.37)%,(95.83±0.59)%,(98.64±1.63)%,and(95.44±1.22)%,respectively.Compared to the Baseline,the average Dice,NSD,clDice,BD,and TD values of the proposed model increased by 0.92%,0.82%,0.92%,1.11%,and 1.60%,respectively.The CAU-Net vascular segmentation model can significantly improve the accuracy and completeness of vascular segmentation.关键词
血管分割/中心线约束/各向异性/注意力机制/双分支模型Key words
vascular segmentation/centerline constraint/anisotropy/attention mechanism/dual-branch model分类
信息技术与安全科学引用本文复制引用
徐晓峰,黄韫栀,徐军..基于各向异性注意力的双分支血管分割模型[J].计算机工程,2024,50(1):348-356,9.基金项目
国家自然科学基金(U1809205,62171230,62101365,61771249) （U1809205,62171230,62101365,61771249）
南京信息工程大学科研启动经费(2022r100) （2022r100）
江苏省双创博士经费. （）
